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Fuzzy Logic for Walking Patterns Based on Surface Electromyography Signals with Different Membership Functions

Abstract— Classifying walking patterns is important in developing assistive robotic devices, especially for lower limb rehabilitation. Recently, Fuzzy Logic (FL) controllers have successfully been applied in grasping and control system for upper limb based on surface Electromyography (EMG) signals. Therefore, this paper evaluates the performance of FL with different membership functions in discriminating walking phases (e.g, stance and swing phases). The accuracy of two widely used membership functions (MF) like triangular and Gaussian is compared to identify their behavior for detecting the phases of walking. In this study, the MATLAB and Simulink toolboxes are used to examine the performance of each MF. Our findings show Gaussian MF gained better performance than the triangular MF with 90% of classification accuracy. Therefore, the Gaussian MF could be the best solution to classify the walking phases in this work.

Index Terms— walking phases, fuzzy logic, pattern recognition, classification.

Nurhazimah Nazmi, Mohd Azizi Abdul Rahman, Dimas Adiputra, Hairi Zamzuri, Saiful Amri Mazlan
Vehicle System Engineering, Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, MALAYSIA
Nurhazimah Nazmi, Yamamoto Shin-ichiroh
Department of Bio-science and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, JAPAN
Siti Anom Ahmad
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, MALAYSIA

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Cite: Nurhazimah Nazmi, Yamamoto Shin-ichiroh, Mohd Azizi Abdul Rahman, Siti Anom Ahmad, Dimas Adiputra, Hairi Zamzuri, Saiful Amri Mazlan, "Fuzzy Logic for Walking Patterns Based on Surface Electromyography Signals with Different Membership Functions," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 636-639, Tokyo, 17-19 June, 2016.